We propose a framework for general probabilistic multi-step time series regression. Specifically, we exploit the expressiveness and temporal nature of Sequence-to-Sequence Neural …
Predictive models benefit from a compact, non-redundant subset of features that improves interpretability and generalization. Modern data sets are wide, dirty, mixed with both numerical …
Document representation using the bag-of-words approach may require bringing the dimensionality of the representation down in order to be able to make effective use of various …
Data preprocessing is an indispensable step in effective data analysis. It prepares data for data mining and machine learning, which aim to turn data into business intelligence or …
Learning Vector Quantization (LVQ) is a group of algorithms applicable to statistical pattern recognition, in which the classes are described by a relatively small number of codebook …
The prevalence of approaches based on gradient boosted trees among the top contestants in the M5 competition is potentially the most eye-catching result. Tree-based methods out-…
Ohjelmistoautomaation tärkeys kasvaa koko ajan yhteiskunnassamme. Tietojärjestelmät ovat kehittyneet ja niissä liikkuvan datan määrä vain kasvaa. Kasvavan datan määrän vuoksi, …
A classification system typically consists of both a feature extractor (preprocessor) and a classifier. These two components can be trained either independently or simultaneously. The …
TIIVISTELMÄ Toimintatapojen muuttaminen ketterämmiksi vaikuttaa olevan työelämässä vallitseva tuore trendi, jolla pyritään tehokkaaseen ja nopeasti muutoksiin reagoivaan …
This work provides a Deep Reinforcement Learning approach to solving a periodic review inventory control system with stochastic vendor lead times, lost sales, correlated demand, and …